The most frequent killer of Americans is cardiovascular disease. Cardiac pathology, such as atrial fibrillation and myocardial ischemia (MI), may be diagnosed by using cardiac electrophysiological (EP) signals and hemodynamic signals (e.g., invasive blood pressure signals). Cardiac arrhythmia detection is typically performed using surface electrocardiogram (ECG) signal, intra-cardiac EP signal and hemodynamic signal analyses based on waveform morphology and time domain parameters.
Currently, known non-invasive clinical methods rely mainly on surface ECG, peripheral capillary oxygen saturation (SPO2), non-invasive blood pressure (NIBP), respiration and temperature signal analysis to determine electrophysiological characteristics and hemodynamic parameters, oximetric blood content, max-min pressure resonance, capnographic and temperature changes information, etc. for monitoring. However, such information does not fully utilize circulation information, such as blood propagation and vibration patterns, vessel wall vibration sound mode, etc.
Most known clinical blood function analyses still rely on catheter technologies (e.g., intra-cardiac blood pressure catheter, Swan-Ganz catheter, etc.), and are usually invasive or partially invasive. In most clinical methods for cardiac signal monitoring, the sensors are active and usually send detecting signals (e.g., ultrasound signal, stimulation signal, etc.) to patient tissue and receive feedback and response signals (e.g., alternating current or AC impedance measurement) for comparison and function diagnosis. These sensors are usually invasive and add unnecessary regulatory and patient safety risk due to, for example, leakage current.
Known methods for cardiac arrhythmia detection focus on qualitative pathology characterization and quantification based on signal time domain amplitude (e.g., ST segmentation elevation in surface ECG signals). Recently, some studies have applied new algorithms for cardiac arrhythmia detection, such as frequency domain parameter, time-frequency distribution mapping, statistical entropy, etc. However, these algorithms typically fail to take into consideration mechanical vibration and sound waveform data associated with the patient's body, which can be useful in quantifying cardiac function.